Natural Order - Complexity - Visions of
the Whole

…

Why do rural families in a nation such as Bangladesh
still produce an
average of seven children apiece, even when birth control is made
freely available—and even when the villagers seem perfectly well
aware of how they're being hurt by the country's immense overpopulation
and stagnant development? Why do they continue in a course of behavior
that's so obviously disastrous?

How did a primordial soup of
amino acids and other simple molecules manage to turn itself into the
first living cell some four billion years ago? There's no way the
molecules could have just fallen together at random; as the
creationists are fond of pointing out, the odds against that happening
are ludicrous. So was the creation of life a miracle? Or was there
something else going on in that primordial soup that we still don't
understand?

Why did individual cells begin to form alliances
some 600 million years ago, thereby giving rise to multicellular
organisms such as seaweed, jellyfish, insects, and eventually humans?
For that matter, why do humans spend so much time and effort organizing
themselves into families, tribes, communities, nations, and societies
of all types? If evolution (or free market capitalism) is really just a
matter of the survival of the fittest, then why should it ever produce
anything other than ruthless competition among individuals? In a world
where nice guys all too often finish last, why should there be any such
thing as trust or cooperation? And why, in spite of everything, do
trust and cooperation not only exist but flourish?

How can
Darwinian natural selection account for such wonderfully intricate
structures as the eye or the kidney? Is the incredibly precise
organization that we find in living creatures really just the result of
random evolutionary accidents? Or has something more been going on for
the past four billion years, something that Darwin didn't know about?

What is life, anyway? Is it nothing more than a
particularly
complicated kind of carbon chemistry? Or is it something more subtle?
And what are we to make of creations such as computer viruses? Are they
just pesky imitations of life—or in some fundamental sense are
they really alive?

What is a mind? How does a three-pound
lump of ordinary matter, the brain, give rise to such ineffable
qualities as feeling, thought, purpose, and awareness?

And
perhaps most fundamentally, why is there something rather than nothing?

The universe started out from the formless miasma of the Big Bang. And
ever since then it's been governed by an inexorable tendency toward
disorder, dissolution, and decay, as described by the second law of
thermodynamics. Yet the universe has also managed to bring forth
structure on every scale: galaxies, stars, planets, bacteria, plants,
animals, and brains. How? Is the cosmic compulsion for disorder matched
by an equally powerful compulsion for order, structure, and
organization? And if so, how can both processes be going on at once?

At
first glance, about the only thing that these questions have in common
is that they all have the same answer: "Nobody knows." Some of them
don't even seem like scientific issues at all. And yet, when you look a
little closer, they actually have quite a lot in common. For example,
every one of these questions refers to a system that is complex, in the
sense that a great many independent agents are interacting with each
other in a great many ways. Think of the quadrillions of chemically
reacting proteins, lipids, and nucleic acids that make up a living
cell, or the billions of interconnected neurons that make up the brain,
or the millions of mutually interdependent individuals who make up a
human society.

In every case, moreover, the very richness of these
interactions allows the system as a whole to undergo spontaneous
self-organization. Thus people trying to satisfy their material needs
unconsciously organize themselves into an economy through myriad
individual acts of buying and selling; it happens without anyone being
in charge or consciously planning it. The genes in a developing embryo
organize themselves in one way to make a liver cell and in another way
to make a muscle cell. Flying birds adapt to the actions of their
neighbors, unconsciously organizing themselves into a flock. Organisms
constantly adapt to each other through evolution, thereby organizing
themselves into an exquisitely tuned ecosystem. Atoms search for a
minimum energy state by forming chemical bonds with each other, thereby
organizing themselves into structures known as molecules. In every
case, groups of agents seeking mutual accommodation and
self-consistency somehow manage to transcend themselves, acquiring
collective properties such as life, thought, and purpose that they
might never have possessed individually.

Furthermore, these complex,
self-organizing systems are adaptive, in that they don't just passively
respond to events the way a rock might roll around in an earthquake.
They actively try to turn whatever happens to their advantage. Thus,
the human brain constantly organizes and reorganizes its billions of
neural connections so as to learn from experience (sometimes, anyway).
Species evolve for better survival in a changing environment—and
so do corporations and industries. And the marketplace responds to
changing tastes and lifestyles, immigration, technological
developments, shifts in the price of raw materials, and a host of other
factors.

Finally, every one of these complex, self-organizing,
adaptive systems possesses a kind of dynamism that makes them
qualitatively different from static objects such as computer chips or
snowflakes, which are merely complicated. Complex systems are more
spontaneous, more disorderly, more alive than that. At the same time,
however, their peculiar dynamism is also a far cry from the weirdly
unpredictable gyrations known as chaos. In the past two decades, chaos
theory has shaken science to its foundations with the realization that
very simple dynamical rules can give rise to extraordinarily intricate
behavior; witness the endlessly detailed beauty of fractals, or the
foaming turbulence of a river. And yet chaos by itself doesn't explain
the structure, the coherence, the self-organizing cohesiveness of
complex systems.

Instead, all these complex systems have somehow
acquired the ability to bring order and chaos into a special kind of
balance. This balance point— often called the edge of
chaos—is were the components of a system never quite lock into
place, and yet never quite dissolve into turbulence, either. The edge
of chaos is where life has enough stability to sustain itself and
enough creativity to deserve the name of life. The edge of chaos is
where new ideas and innovative genotypes are forever nibbling away at
the edges of the status quo, and where even the most entrenched old
guard will eventually be overthrown. The edge of chaos is where
centuries of slavery and segregation suddenly give way to the civil
rights movement of the 1950s and 1960s; where seventy years of Soviet
communism suddenly give way to political turmoil and ferment; where
eons of evolutionary stability suddenly give way to wholesale species
transformation. The edge of chaos is the constantly shifting battle
zone between stagnation and anarchy, the one place where a complex
system can be spontaneous, adaptive, and alive.

Complexity,
adaptation, upheavals at the edge of chaos—these common themes
are so striking that a growing number of scientists are convinced that
there is more here than just a series of nice analogies. The movement's
nerve center is a think tank known as the Santa Fe Institute, which was
founded in the mid-1980s and which was originally housed in a rented
convent in the midst of Santa Fe's art colony along Canyon Road.
(Seminars were held in what used to be the chapel.) The researchers who
gather there are an eclectic bunch, ranging from pony-tailed graduate
students to Nobel laureates such as Murray Gell-Mann and Philip
Anderson in physics and Kenneth Arrow in economics. But they all share
the vision of an underlying unity, a common theoretical framework for
complexity that would illuminate nature and humankind alike. They
believe that they have in hand the mathematical tools to create such a
framework, drawing from the past twenty years of intellectual ferment
in such fields as neural networks, ecology, artificial intelligence,
and chaos theory. They believe that their application of these ideas is
allowing them to understand the spontaneous self-organizing dynamics of
the world in a way that no one ever has before— with the
potential for immense impact on the conduct of economics, business, and
even politics. They believe that they are forging the first rigorous
alternative to the kind of linear, reductionist thinking that has
dominated science since the time of Newton—and that has now gone
about as far as it can go in addressing the problems of our modern
world. They believe they are creating, in the words of Santa Fe
Institute founder George Cowan, "the sciences of the twenty-first
century."

This is their story.

COMPLEXITY

Master of the Game winners
by lottery, with the highest probability of winning going to the
highest bidders. The chosen classifiers would post their messages, and
the cycle would repeat. Complex? Holland couldn't deny it. As
things
stood, moreover, the auction simply replaced arbitrary conflict
resolution strategies with arbitrary plausibility values. But assuming
for the moment that the system could. somehow learn these plausibility
values from experience, then the auction would eliminate the central
arbiter and give Holland exactly what he wanted. Not every classifier
could win: the bulletin board was big, not infinite. Nor would the race
always go to the swift: even Elvis might get a chance to post his
message if he got a lucky break. But on the average, control over the
system's behavior would automatically be given to the strongest and
most plausible hypotheses, with off-the-wall hypotheses appearing just
often enough to give the system a little spontaneity. And if some of
those hypotheses were inconsistent, well, that shouldn't be a crisis
but an opportunity, a chance for the system to learn from experience
which ones are more plausible.

So once again, it all came back to
learning: How were the classifiers supposed to prove their worth and
earn their plausibility values?
To Holland, the obvious answer was
to implement a kind of Hebbian reinforcement. Whenever the agent does
something right and gets a positive feedback from the environment, it
should strengthen the classifiers responsible. Whenever it does
something wrong, it should likewise weaken the classifiers responsible.
And either way, it should ignore the classifiers that were irrelevant.

The
trick, of course, was to figure out which classifiers were which. The
agent couldn't just reward the classifiers that happen to be active at
the moment of payoff. That would be like giving all the credit for a
touchdown to the player who happened to carry the ball across the goal
line—and none to the quarterback who called the play and passed
him the ball, or to the linemen who blocked the other team and opened
up a gap for him to run through, or to anyone who carried the ball in
previous plays. It would be like giving all the credit for a victory in
chess to the final move that trapped your opponent's king, and none to
the crucial gambit many moves before that set up your whole endgame.
And yet, what was the alternative? If the agent had to anticipate the
payoff in order to reward the correct classifiers, how was it supposed
to do so without being preprogrammed? How was it supposed to learn the
value of these stage-setting moves without knowing about them already?

Good
questions. Unfortunately, the general idea of Hebbian reinforcement was
too broad-brush to provide any answers. Holland was at a loss—
until one day he happened to think back on the basic economics course
he'd taken at MIT from Paul Samuelson, author of the famous economics
textbook, and realized that he'd almost solved the problem already. By
auctioning off space on the bulletin board, he had created a kind of
marketplace within the system. By allowing the classifiers to bid on
the basis of their strength, he had created a currency. So why not take
the next step? Why not create a full-fledged free-market economy, and
allow the reinforcement to take place through the profit motive?

Why
not, indeed? The analogy was obvious when you finally saw it. If you
thought of the messages posted on the bulletin board as being goods and
services up for sale, Holland realized, then you could think of the
classifiers as being firms that produce those goods and services. And
when a classifier sees a message satisfying its if-conditions and makes
a bid, then you could think of it as a firm trying to purchase the
supplies it needs to make its product. All he had to do to make the
analogy perfect was to arrange for each classifier to pay for the
supplies it used. When a classifier won the right to post its message,
he decided, it would transfer some of its strength to its suppliers:
namely, the classifiers responsible for posting the messages that
triggered it. In the process, the classifier would then be weakened.
But it would have a chance to recoup its strength and even make a
profit during the next round of bidding, when its own message went on
the market.

And where would the wealth ultimately come from? From
the final consumer, of course: the environment, the source of all
payoffs to the system. Except that now, Holland realized, it would be
perfectly all right to reward the classifiers that happen to be active
at the moment of payoff. Since each classifier pays its suppliers, the
marketplace will see to it that the rewards propagate through the whole
collection of classifiers and produce exactly the kind of automatic
reward and punishment he was looking for. "If you produce the right
intermediate product, then you'll make a profit," he says. "If not,
then nobody will buy it and you'll go bankrupt." All the classifiers
that lead to effective action will be strengthened, and yet none of the
stage-setting classifiers will be neglected. Over time, in fact, as the
system gains experience and gets feedback from the environment, the
strength of each classifier will come to match its true value to the
agent.
Holland dubbed this portion of his adaptive agent the
"bucket-brigade" algorithm because of the way it passed reward from
each classifier to the previous classifier. It was directly analogous
to the strengthening of synapses in Hebb's theory of the
brain—or, for that matter, to the kind of reinforcement used to
train a simulated neural network in a computer. And when he had it,
Holland knew he was almost home. Economic reinforcement via the profit
motive was an enormously powerful organizing force, in much the same
way that Adam Smith's Invisible Hand was enormously powerful in the
real economy. In principle, Holland realized, you could start the
system off with a set of totally random classifiers, so that the agent
just thrashed around like the software equivalent of a newborn baby.
And then, as the environment reinforced certain behaviors and as the
bucket brigade did its work, you could watch the classifiers organize
themselves into coherent sequences that would produce at least a
semblance of the desired behavior. Learning, in short, would be built
into the system from the beginning.

So, Holland was almost
home—but not quite. By constructing the bucket brigade algorithm
on top of the basic rule-based system, Holland had given his adaptive
agent one form of learning. But there was another form still missing.
It was the difference between exploitation and exploration. The
bucket-brigade algorithm could strengthen the classifiers that the
agent already possessed. It could hone the skills that were already
there. It could consolidate the gains that had already been made. But
it couldn't create anything new. By itself, it could only lead the
system into highly optimized mediocrity. It had no way to explore the
immense space of possible new classifiers .

This, Holland decided,
was a job for the genetic algorithm. When you thought about it, in
fact, the Darwinian metaphor and the Adam Smith metaphor fit together
quite nicely: Firms evolve over time, so why shouldn't classifiers?

Holland
certainly wasn't surprised by this insight; he'd had the genetic
algorithm in the back of his mind all along. He'd been thinking about
it when he first set up the binary representation of classifiers. A
classifier might be paraphrased in English as something like, "If there
are two messages with the patterns 1###0#00 and 0#00####, then post the
message 01110101." In the computer, however, its various parts would be
concatenated together and written simply as a string of bits:
"1###0#000#00####01110101." And to the genetic algorithm, that looked
just like a digital chromosome. So the algorithm could be carried out
in exactly the same way. Most of the time, the classifiers would
merrily buy and sell in their digital marketplace as before. But every
so often, the system would select a pair of the strongest classifiers
for reproduction. These classifiers would reshuffle their digital
building blocks by sexual exchange to produce a pair of offspring. The
offspring would replace a pair of weak classifiers. And then the
offspring would have a chance to prove their worth and grow stronger
through the bucket-brigade algorithm.

The upshot was that the
population of rules would change and evolve over time, constantly
exploring new regions of the space of possibilities. And there you
would have it: by adding the genetic algorithm as a third layer on top
of the bucket brigade and the basic rule-based system, Holland could
make an adaptive agent that not only learned from experience but could
be spontaneous and creative.

And all he had to do was to turn it into a working program.

Holland
started coding the first classifier system around 1977. And oddly
enough, it didn't turn out to be as straightforward a job as he had
hoped. "I really thought that in a couple of months I'd have something
up and running that was useful to me," he says. "Actually, it was the
better part of a year before I was fully satisfied."

On the other
hand, he didn't exactly make things easy for himself. He coded that
first classifier system in true Holland style: by himself. At home. In
hexadecimal code, the same kind that he'd written for the Whirlwind
thirty years earlier. On a Commodore home computer.
Holland's BACH
colleagues still roll their eyes when they tell this story. The whole
campus was crawling with computers: VAXs, mainframes, even high-powered
graphics workstations. Why a Commodore? And hex! Almost nobody wrote in
hex anymore. If you were really a hard-core computer jock trying to
squeeze the last ounce of performance out of a machine, you might write
in something called assembly language, which at least replaced the
numbers with mnemonics like MOV, JMZ, and SUB. Otherwise, you went with
a high-level language such as PASCAL, C, FORTRAN, or
LISP—something that a human being could hope to understand.
Cohen, in particular, remembers arguing long and hard with Holland:
Who's going to believe that this thing works if it's written in
alphanumeric gibberish? And even if anybody does believe you, who's
going to use a classifier system if it only runs on a home computer?
Holland
eventually had to concede the point—although it was well into the
early 1980s before he agreed to hand over the classifier system code to
a graduate student, Rick Riolo, who transformed it into a
general-purpose senses the
analogies, but it's more difficult to make them precise," he says.
"That's another area where somebody needs to do some careful cross
comparisons, analogous to the Rosetta Stone paper."

Meanwhile, says
Farmer, it's even less clear whether the edge-of-chaos idea applies to
coevolutionary systems. When you get to something like an ecosystem or
an economy, he says, it's not obvious how concepts like order, chaos,
and complexity can even be defined very precisely, much less a phase
transition between them. Nonetheless, he says, there's something about
the edge-of-chaos principle that still feels right. Take the former
Soviet Union, he says: "It's now pretty clear that the totalitarian,
centralized approach to the organization of society doesn't work very
well." In the long run, the system that Stalin built was just too
stagnant, too locked in, too rigidly controlled to survive. Or look at
the Big Three auto makers in Detroit in the 1970s. They had grown so
big and so rigidly locked in to certain ways of doing things that they
could barely recognize the growing challenge from Japan, much less
respond to it.

On the other hand, says Farmer, anarchy doesn't work
very well, either— as certain parts of the former Soviet Union
seemed determined to prove in the aftermath of the breakup. Nor does an
unfettered laissez-faire system: witness the Dickensian horrors of the
Industrial Revolution in England or, more recently, the savings and
loan debacle in the United States. Common sense, not to mention recent
political experience, suggests that healthy economies and healthy
societies alike have to keep order and chaos in balance—and not
just a wishy-washy, average, middle-of-the road kind of balance,
either. Like a living cell, they have to regulate themselves with a
dense web of feedbacks and regulation, at the same time that they leave
plenty of room for creativity, change, and response to new conditions.
"Evolution thrives in systems with a bottom-up organization, which
gives rise to flexibility," says Farmer. "But at the same time,
evolution has to channel the bottom-up approach in a way that doesn't
destroy the organization. There has to be a hierarchy of
control—with information flowing from the bottom up as well as
from the top down." The dynamics of complexity at the edge of chaos, he
says, seems to be ideal for this kind of behavior.

The Growth of Complexity

In
any case, says Farmer, "at a vague, heuristic level we think we know
something about the domain where this interesting organizational
phenominum…

Shortly
before Christmas of 1989, as Brian Arthur drove west from Santa Fe with
a car packed full of books and clothes for his return home to Stanford,
he found himself staring straight into a spectacular New Mexico sunset
that bathed the desert in a vast red glow. "I thought, 'This is too
bloody romantic to be true!' " he laughs.

But appropriate. "I had
been at the institute just about eighteen months at that time," he
says, "and I felt that I needed to go home—to write, and think,
and get things clear in my mind. I was just loaded down with ideas. I'd
felt that I was learning at Santa Fe more in a month than I would have
in a year at Stanford. The experience had almost been too rich. And yet
it was a wrench to leave. I felt very, very, very sad, in a good way,
and very nostalgic. The whole scene—the desert, the light, the
sunset—brought home to me that those eighteen months might well
have been the high point of my scientific life, and they were over.
That time would not be easily recaptured. I knew other people would
come and follow up. I knew I could probably go back—even go back
and run the economics program again in some future years. But I
suspected that the institute might never be the same. I felt lucky to
have been in on a golden time."
The Tao of Complexity

Three years
later, sitting in his corner office overlooking the tree-shaded
waldways of Stanford University, the Dean and Virginia Morrison
Professor of Population Studies and Economics admits that he still
hasn't gotten the Santa Fe experience completely clear in his mind.
"I'm beginning to appreciate it more as time passes, " says Arthur.
"But I think the story of what's been accomplished in Santa Fe is still
very much unfolding."

Fundamentally, he says, he's come to realize
that the Santa Fe Institute was and is a catalyst for changes that
would have taken place in any case— but much more slowly.
Certainly that was the case for the economics program, which continued
after his departure under the joint directorship of Minnesota's David
Lane and Yale's John Geanakoplos. "By about 1985," says Arthur, "it
seems to me that all sorts of economists were getting antsy, starting
to look around and sniff the air. They sensed that the conventional
neoclassical framework that had dominated over the past generation had
reached a high water mark. It had allowed them to explore very
thoroughly the domain of problems that are treatable by static
equilibrium analysis. But it had virtually ignored the problems of
process, evolution, and pattern formation—problems where things
were not at equilibrium, where there's a lot of happenstance, where
history matters a great deal, where adaptation and evolution might go
on forever. Of course, the field had kind of gotten stymied by that
time, because theories were not held to be theories in economics unless
they could be fully mathematized, and people only knew how to do that
under conditions of equilibrium. And yet some of the very best
economists were sensing that there had to be other things going on and
other directions that the subject could go in.

"What Santa Fe did
was to act as a gigantic catalyst for all that. It was a place where
very good people—people of the caliber of Frank Hahn and Ken
Arrow—could come and interact with people like John Holland and
Phil Anderson, and over a period of several visits there realize, Yes!
We can deal with inductive learning rather than deductive logic, we can
cut the Gordian knot of equilibrium and deal with open-ended evolution
because many of these problems have been dealt with by other
disciplines. Santa Fe provided the jargon, the metaphors, and the
expertise that you needed in order to get the techniques started in
economics. But more than that, Santa Fe legitimized this different
vision of economics. Because when word got around that people like
Arrow and Hahn and Sargent and others were writing papers of this sort,
then it became perfectly reasonable and perfectly kosher for others to
do so."

Arthur sees evidence for that development every time he goes
to an economics meeting these days. "The people who were interested in
process and change in the economy were there all along," he says.
Indeed, many of the essential ideas were championed by the great
Austrian economist Joseph Schumpeter as far back as the 1920s and
1930s. "But my sense is that in the past four or five years, the people
who think this way have gotten much more confident. They aren't
apologetic any more about just being able to give wordy, qualitative
descriptions of economic change. Now they're armed. They have
technique. They form a growing movement that is becoming part of the
neoclassical mainstream everywhere."

That movement has certainly
made his own life easier, notes Arthur. His ideas on increasing
returns, once virtually unpublishable, now have a following. He finds
himself getting invitations to give this or that distinguished lecture
in far-off places. In 1989 he was invited to write a feature article on
increasing-returns economics for Scientific American. "That was one of
the biggest thrills," he says. And that article, published in February
1990, helped him become a co-winner of the International Schumpeter
Society's 1990 Schumpeter Prize for the best research on evolutionary
economics.

For Arthur, however, the most gratifying assessment of
the Santa Fe approach came in September 1989, as Ken Arrow was
summarizing a big, week-long workshop that had reviewed the program's
progress to date. At the time, ironically, Arthur barely heard what
Arrow was saying. That l noontime, he says, as he'd headed out the
front door of the convent on his way to lunch, he'd managed to trip and
sprain his ankle terribly. He'd spent that whole afternoon in the
convent's chapel-turned-conference room listening to the closing
session of the workshop through a haze of pain, with his foot carefully
wrapped by Dr. Kauffman and propped up with a bag of ice on the chair
in front of him. In fact, the full impact of Arrow's words only hit him
a few days later, after he'd defied all advice of doctors, colleagues,
and wife and hobbled off to a long-planned conference in Irkutsk, on
the shores of Lake Baikal in Siberia.

"It was one of these flashes
of extreme clarity you get at three in the morning," he says. "The
Aeroflot jet was just coming into Irkutsk, and i there was this guy
riding a bicycle down the runway, waving a light stick to show us where
to taxi. And when I thought about what Arrow had said in his closing
summary, it finally struck home. He said, 'I think we can safely say we
have another type of economics here. One type is the standard stuff
that we're all familiar with'—he was too modest to call it the
Arrow-Debreu system, but he basically meant the neoclassical, general
equilibrium theory—'and then this other type, the Santa Fe-style
evolutionary economics.' He made it clear that, to his mind, what the
program had demonstrated in a year was that this was another valid way
to do economics, equal in status to the traditional theory. It wasn't
that the standard formulation was wrong, he said, but that we were
exploring into a new way of looking at parts of the economy that are
not amenable to conventional methods. So this new approach was
complementary to the standard ones. He also said that we didn't know
where this new sort of economics was taking us. It was the beginnings
of a research program. But he found it very interesting and exciting.
"That
pleased me enormously," says Arthur. "But Arrow said a second thing
also. He compared the Santa Fe program of research with the Cowles
Foundation program that he had been associated with in the early 1950s.
And he said that the Santa Fe approach seemed to be much more accepted
at this stage, given that it's now at most two years old, than the
Cowles Foundation group had been at the same point. Well, I was amazed
to hear that, and tremendously flattered. Because the Cowles Foundation
people were the Young Turks of their day—Arrow, Koopmans, Debreu,
Klein, Hurwicz, et cetera. Four of them got Nobel Prizes, with maybe a
few more to come. They were the people who mathematized economics. They
were the people who had set the agenda for the following generations.
They were the people who had actually revolutionized the field."

From
the Santa Fe Institute's point of view, of course, this effort to
catalyze a sea change in economics is only a part of its effort to
catalyze the complexity revolution in science as a whole. That quest
may yet prove quixotic, says Arthur. But nonetheless, he's convinced
that George Cowan, Murray Gell-Mann, and the others have gotten hold of
exactly the right set of issues.

"Nonscientists tend to think that
science works by deduction," he says. "But actually science works
mainly by metaphor. And what's happening is that the kinds of metaphor
people have in mind are changing." To put it in perspective, he says,
think of what happened to our view of the world with the advent of Sir
Isaac Newton. "Before the seventeenth century," he says, "it was a
world of trees, disease, human psyche, and human behavior. It was messy
and organic. The heavens were also complex. The trajectories of the
planets seemed arbitrary. Trying to figure out what was going on in the
world was a matter of art. But then along comes Newton in the 1660s. He
devises a few laws, he devises the differential calculus—and
suddenly the planets are seen to be moving in simple, predictable
orbits!

"This had an incredibly profound effect on people's psyche,
right up to the present," says Arthur. "The heavens—the habitat
of God—had been explained, and you didn't need angels to push
things around anymore. You didn't need God to hold things in place. So
in the absence of God, the age became more secular. And yet, in the
face of snakes and earthquakes, storms and plagues, there was still a
profound need to know that something had it all under control. So in
the Enlightenment, which lasted from about 1680 all through the 1700s,
the era shifted to a belief in the primacy of nature: if you just left
things alone, nature would see to it that everything worked out for the
common good."

The metaphor of the age, says Arthur, became the
clockwork motion of the planets: a simple, regular, predictable
Newtonian machine that would run of itself. And the model for the next
two and a half centuries of reductionist science became Newtonian
physics. "Reductionist science tends to say, 'Hey, the world out there
is complicated and a mess—but look! Two or three laws reduce it
all to an incredibly simple system!'

"So all that remained was for
Adam Smith, at the height of the Scottish Enlightenment around
Edinburgh, to understand the machine behind the economy," says Arthur.
"In 1776, in The Wealth of Nations, he made the case that if you left
people alone to pursue their individual interests, the 'Invisible Hand'
of supply and demand would see to it that everything, worked out for
the common good." Obviously, this was not the whole story:. Smith
himself pointed to such nagging problems as worker alienation and
exploitation. But there was so much about his Newtonian view of the:
economy that was simple and powerful and right that it has dominated
Western economic thought ever since. "Smith's idea was so brilliant
that it just dazzled us," says Arthur. "Once, long ago, the economist
Kenneth Boulding asked me, 'What would you like to do in economics?'
Being young and brash, I said very immodestly, 'I want to bring
economics into the twentieth century.' He looked at me and said, 'Don't
you think you should bring it into the eighteenth century first?' "

In
fact, says Arthur, he feels that economics in the twentieth century has
lagged about a generation behind a certain loss of innocence in all the
sciences. As the century began, for example, philosophers such as
Russell, i, Whitehead, Frege, and Wittgenstein set out to demonstrate
that all of mathematics could be founded on simple logic. They were
partly right.. Much of it can be. But not all: in the 1930s, the
mathematician Kurt Godel showed that even some very simple mathematical
systems—arithmetic, for example—are inherently incomplete.
They always contain statements that cannot be proved true or false
within the system, even in principle. At about the same time (and by
using essentially the same argument), the logician Alan Turing showed
that even very simple computer programs can be undecidable: you can't
tell in advance whether the computer will reach an answer or not. In
the 1960s and 1970s, physicists got much the same message from chaos
theory: even very simple equations can produce results that are
surprising and essentially unpredictable. Indeed, says Arthur, that
message has been repeated in field after field. "People realized that
logic and philosophy are messy, that language is messy, that chemical
kinetics is messy, that physics is messy, and finally that the economy
is naturally messy. And it's not that this is a mess created by the
dirt that's on the microscope glass. It's that this mess is inherent in
the systems themselves. You can't capture any of them and confine them
to a neat box of logic."

The result, says Arthur, has been the
revolution in complexity. "In a sense it's the opposite of
reductionism. The complexity revolution began the first time someone
said, 'Hey, I can start with this amazingly simple system, and
look—it gives rise to these immensely complicated and
unpredictable consequences. ' " Instead of relying on the Newtonian
metaphor of clockwork predictability, complexity seems to be based on
metaphors more closely akin to the growth of a plant from a tiny seed,
or the unfolding of a computer program from a few lines of code, or
perhaps even the organic, self-organized flocking of simpleminded
birds. That's certainly the kind of metaphor that Chris Langton has in
mind with artificial life: his whole point is that complex, lifelike
behavior is the result of simple rules unfolding from the bottom up.
And it's likewise the kind of metaphor that influenced Arthur in the
Santa Fe economics program: "If I had a purpose, or a vision, it was to
show that the messiness and the liveliness in the economy can grow out
of an incredibly simple, even elegant theory. That's why we created
these simple models of the stock market where the market appears moody,
shows crashes, takes off in unexpected directions, and acquires
something that you could describe as a personality."

While he was
actually at the institute, ironically, Arthur had almost no time at all
for Chris Langton's artificial life, or the edge of chaos, or the
hypothetical new second law. The economics program was taking up 110
percent of his workday as it was. But what he did hear he found
fascinating. It seemed to him that artificial life and the rest
captured something essential about the spirit of the institute. "Martin
Heidegger once said that the fundamental philosophical question is
being," notes Arthur. "What are we doing here as conscious entities?
Why isn't the universe just a turbulent mess of particles tumbling
around each other? Why are there structure, form, and pattern? Why is
consciousness possible at all?" Very few people at the institute were
grappling with that problem quite as directly as Langton, Kauffman, and
Farmer were. But in one way or another, says Arthur, he sensed that
everyone was working on a piece of it.
Furthermore, he felt that the
ideas resonated strongly with what he and his coconspirators were
trying to accomplish in economics. When you look at the subject through
Chris Langton's phase transition glasses, for example, all of
neoclassical economics is suddenly transformed into a simple assertion
that the economy is deep in the ordered regime, where the market is
always in equilibrium and things change slowly if at all. The Santa Fe
approach is likewise transformed into a simple assertion that the
economy is at the edge of chaos, where agents are constantly adapting
to each other and things are always in flux. Arthur always knew which
assertion he thought was more realistic.

Like other Santa Fe folk,
Arthur is hesitant when it comes to speculating about the larger
meaning of all this. The results are still so—embryonic. And it's
entirely too easy to come off sounding New Age and flaky. But like
everyone else, he can't help thinking about the larger meaning.

You
can look at the complexity revolution in almost theological terms, I he
says. "The Newtonian clockwork metaphor is akin to standard
Protestantism. Basically there's order in the universe. It's not that
we rely on God for order. That's a little too Catholic. It's that God
has arranged the world so that the order is naturally there if we
behave ourselves. If we act as individuals in our own right, if we
pursue our own righteous self-interest and work hard, and don't bother
other people, then the natural equilibrium :, of the world will assert
itself. Then we get the best of all possible worlds— the one we
deserve. That's probably not quite theological, but it's the impression
I have of one brand of Christianity.

"The alternative—the
complex approach—is total Taoist. In Taoism there is no inherent
order. 'The world started with one, and the one became two, and the two
became many, and the many led to myriad things.' The universe in Taoism
is perceived as vast, amorphous, and ever-changing. You can never nail
it down. The elements always stay the same, yet they're always
rearranging themselves. So it's like a kaleidoscope: the world is a
matter of patterns that change, that partly repeat, but never quite
repeat, that are always new and different.

"What is our relation to
a world like that? Well, we are made of the same elemental
compositions. So we are a part of this thing that is never changing and
always changing. If you think that you're a steamboat and can go up the
river, you're kidding yourself. Actually, you're just the captain of a
paper boat drifting down the river. If you try to resist, you're not
going to get anywhere. On the other hand, if you quietly observe the
flow, realizing that you're part of it, realizing that the flow is
ever-changing and always leading to new complexities, then every so
often you can stick an oar into the river and punt yourself from one
eddy to another.
"So what's the connection with economic and
political policy? Well, in a policy context, it means that you observe,
and observe, and observe, and occasionally stick your oar in and
improve something for the better. It means that you try to see reality
for what it is, and realize that the game you are in keeps changing, so
that it's up to you to figure out the current rules of the game as it's
being played. It means that you observe the Japanese like hawks, you
stop being naive, you stop appealing for them to play fair, you stop
adhering to standard theories that are built on outmoded assumptions
about the rules of play, you stop saying, 'Well, if only we could reach
this equilibrium we'd be in fat city.' You just observe. And where you
can make an effective move, you make a move."
Notice that this is
not a recipe for passivity, or fatalism, says Arthur. "This is a
powerful approach that makes use of the natural nonlinear dynamics of
the system. You apply available force to the maximum effect. You don't
waste it. This is exactly the difference between Westmoreland's
approach in South Vietnam versus the North Vietnamese approach.
Westmoreland would go in with heavy forces and artillery and barbed
wire and burn the villages. And the North Vietnamese would just recede
like a tide. Then three days later they'd be back, and no one knew
where they came from. It's also the principle that lies behind all of
Oriental martial arts. You don't try to stop your opponent, you let him
come at you—and then give him a tap in just the right direction
as he rushes by. The idea is to observe, to act courageously, and to
pick your timing extremely well."

Arthur is reluctant to get into
the implications of all this for policy issues. But he does remember
one small workshop that Murray Gell-Mann persuaded him to cochair in
the fall of 1989, shortly before he left the institute. The purpose of
the workshop was to look at what complexity might have to say about the
interplay of economics, environmental values, and public policy in a
region such as Amazonia, where the rain forest is being cleared for
roads and farms at an alarming rate. The answer Arthur gave during his
own talk was that you can approach policy-making for the rain forest
(or for any other subject) on three different levels.

The first
level, he says, is the conventional cost-benefit approach: What are the
costs of each specific course of action, what are the benefits, and how
do you achieve the optimum balance between the two? "There is a place
for that kind of science," says Arthur. "It does force you to think
through the implications of the alternatives. And certainly at that
meeting we had a number of people arguing the costs and benefits of
rain forests. The trouble is that this approach generally assumes that
the problems are well defined, that the options are well defined, and
that the political wherewithal is there, so that the analyst's job is
simply to put numbers on the costs and benefits of each alternative.
It's as though the world were a railroad switch yard: We're going down
this one track, and we have switches we can turn to guide the train
onto other tracks." Unfortunately for the standard theory, however, the
real world is almost never that well defined—particularly when it
comes to environmental issues. All too often, the apparent objectivity
of cost-benefit analyses is the result of slapping arbitrary numbers ;
on subjective judgments, and then assigning the value of zero to the
things that nobody knows how to evaluate. "I ridicule some of these
cost-benefit analyses in my classes," he says. "The 'benefit' of having
spotted owls is ' defined in terms of how many people visit the forest,
how many will see a spotted owl, and what's it worth to them to see a
spotted owl, et cetera. It's all the greatest rubbish. This type of
environmental cost-benefit analysis ; makes it seem as though we're in
front of the shop window of nature looking in, and saying, 'Yes, we
want this, or this, or this'—but we're not inside, we're not part
of it. So these studies have never appealed to me. By asking only what
is good for human beings, they are being presumptuous and arrogant.

The
second level of policy-making is a full institutional-political
analysis, says Arthur: figuring out who's doing what, and why. "Once
you start to: do that for, say, the Brazilian rain forest, you find
that there are various players: landowners, settlers, squatters,
politicians, rural police, road builders, indigenous peoples. They
aren't out to get the environment, but they are all playing this
elaborate, interactive Monopoly game, in which the environment is being
deeply affected. Moreover, the political system isn't some exogenous
thing that stands outside the game. The political system is actually an
outcome of the game—the alliances and coalitions that form as a
result of it."

In short, says Arthur, you look at the system as a
system, the way a Taoist in his paper boat would observe the complex,
ever-changing river. Of course, a historian or a political scientist
would look at the situation this way instinctively. And some beautiful
studies in economics have recently started to take this approach. But
at the time of the workshop in 1989, he says, the idea still seemed to
be a revelation to many economists. "In my. talk I put in a strong plea
for this kind of analysis," he says. "If you really want to get deeply
into an environmental issue, I told them, you have to ask these
questions of who has what at stake, what alliances are likely to form,
and basically understand the situation. Then you might find certain
points at which intervention may be possible.
"So all of that is
leading up to the third level of analysis," says Arthur. "At this level
we might look at what two different world views have to say about
environmental issues. One of these is the standard equilibrium
viewpoint that we've inherited from the Enlightenment—the idea
that there's a duality between man and nature, and that there's a
natural equilibrium between them that's optimal for man. And if you
believe this view, then you can talk about 'the optimization of policy
decisions concerning environmental resources,' which was a phrase I got
from one of the earlier speakers at the workshop.
"The other
viewpoint is complexity, in which there is basically no duality between
man and nature," says Arthur. "We are part of nature ourselves We're in
the middle of it. There's no division between doers and done-to because
we are all part of this interlocking network. If we, as humans, try to
take action in our favor without knowing how the overall system will
adapt—like chopping down the rain forest—we set in motion a
train of events that will likely come back and form a different pattern
for us to adjust to, like global climate change.

"So once you drop
the duality," he says, "then the questions change. You can't then talk
about optimization, because it becomes meaningless. It would be like
parents trying to optimize their behavior in terms of 'us versus the
kids,' which is a strange point of view if you see yourself as a
family. You have to talk about accommodation and
coadaptation—what would be good for the family as a whole.

"Basically,
what I'm saying is not at all new to Eastern philosophy. It's never
seen the world as anything else but a complex system. But it's a world
view that, decade by decade, is becoming more important in the
West— both in science and in the culture at large. Very, very
slowly, there's been a gradual shift from an exploitative view of
nature—man versus nature— to an approach that stresses the
mutual accommodation of man and nature. What has happened is that we're
beginning to lose our innocence, or naiveté, about how the world
works. As we begin to understand complex systems, we begin to
understand that we're part of an ever-changing, interlocking,
nonlinear, kaleidoscopic world.

"So the question is how you maneuver
in a world like that. And the answer is that you want to keep as many
options open as possible. You go for viability, something that's
workable, rather than what's 'optimal.' A lot of people say to that,
'Aren't you then accepting second best?' No, you're not, because
optimization isn't well defined anymore. What you're trying to do is
maximize robustness, or survivability, in the face of an ill-defined
future. And that, in turn, puts a premium on becoming aware of
nonlinear relationships and causal pathways as best we can. You observe
the world very, very carefully, and you don't expect circumstances to
last."

So what is the role of the Santa Fe Institute in all this?
Certainly not to Y become another policy think tank, says Arthur,
although there always seem to be a few people who expect it to. No, he
says, the institute's role is to help us look at this ever-changing
river and understand what we're seeing.

"If you have a truly complex
system," he says, "then the exact patterns are not repeatable. And yet
there are themes that are recognizable. In history, for example, you
can talk about 'revolutions,' even though one revolution might be quite
different from another. So we assign metaphors. It turns out that an
awful lot of policy-making has to do with finding the appropriate
metaphor. Conversely, bad policy-making almost always involves finding
inappropriate metaphors. For example, it may not be appropriate to
think about a drug 'war,' with guns and assaults.

"So from this
point of view, the purpose of having a Santa Fe Institute is that it,
and places like it, are where the metaphors and a vocabulary are ,i
being created in complex systems. So if somebody comes along with a
beautiful study on the computer, then you can say 'Here's a new
metaphor. Let's call this one the edge of chaos,' or whatever. So what
the SFI will do, if it studies enough complex systems, is to show us
the kinds of patterns we might observe, and the kinds of metaphor that
might be appropriate for systems that are moving and in process and
complicated, rather than the metaphor of clockwork.

"So I would
argue that a wise use of the SFI is to let it do science," he says. "To
make it into a policy shop would be a great mistake. It would cheapen
the whole affair. And in the end it would be counterproductive, because
what we're missing at the moment is any precise understanding of how
complex systems operate. This is the next major task in science for the
next 50 to 100 years."

"I think there's a personality that goes with
this kind of thing," Arthur says. "It's people who like process and
pattern, as opposed to people who are comfortable with stasis and
order. I know that every time in my life that I've run across simple
rules giving rise to emergent, complex messiness I've just said, 'Ah,
isn't that lovely!' And I think that sometimes, when other people run
across it, they recoil."

In about 1980, he says, at a time when he
was still struggling to articulate his own vision of a dynamic,
evolving economy, he happened to read a book by the geneticist Richard
Lewontin. And he was struck by a passage in which Lewontin said that
scientists come in two types. Scientists of the first type see the
world as being basically in equilibrium. And if untidy forces sometimes
push a system slightly out of equilibrium, then they feel the whole
trick is to push it back again. Lewontin called these scientists
"Platonists," after the renowned Athenian philosopher who declared that
the messy, imperfect objects we see around us are merely the
reflections of perfect "archetypes."

Scientists of the second type,
however, see the world as a process of flow and change, with the same
material constantly going around and around in endless combinations.
Lewontin called these scientists "Heraclitians," after the Ionian
philosopher who passionately and poetically argued that the world is in
a constant state of flux. Heraclitus, who lived nearly a century before
Plato, is famous for observing that "Upon those who step into the same
rivers flow other and yet other waters," a statement that Plato himself
paraphrased as "You can never step into the same river twice."

"When
I read what Lewontin said," says Arthur, "it was a moment of
revelation. That's when it finally became clear to me what was going
on. I thought to myself, 'Yes! We're finally beginning to recover from
Newton. ' "

The Hair Shirt

Meanwhile, at about the same time that
Brian Arthur was driving off into the sunset, the Heraclitian-in-chief
back in Santa Fe was getting ready to call it quits. For all the
undeniable success of the economics program, and for all the
intellectual ferment over the edge of chaos, artificial life, and the
rest, George Cowan was acutely aware that the institute's permanent
endowment fund still stood at zero. And after six years, he was tired
of constantly begging people for operating cash. He was tired of
fretting over the economics program, lest it become the 800-pound
gorilla that took over the institute. And speaking of 800-pound
gorillas, he was tired of the endless contest of wills with Murray
Gell-Mann to define what the Santa Fe Institute was all
about—including, not incidentally, what the complexity revolution
could tell us about building a more sustainable future for the human
race. Cowan was just—tired. Now that he'd gotten the Santa Fe
Institute up and running, he wanted to spend the time he had left in
life working on the science of the institute, this strange new science
of complexity. So at the first opportunity—the annual meeting of
the institute's board of trustees in …